Eye in the Sky: Real-Time Drone Surveillance System (DSS) for Violent Individuals Identification Using ScatterNet Hybrid Deep Learning Network

Singh, Amarjot; Patil, Devendra; Omkar, S. N.

doi:10.1109/cvprw.2018.00214

Cited by 117 publications

(58 citation statements)

References 25 publications

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“…Currently, UAV surveillance has been upgraded with many more features of self-controlling, analysis and data processing by integrating UAVs with artificial intelligence (AI) [3] . Using these technologies, UAVs can be trained to perform particular tasks by processing large amounts of images and videos and simultaneously identifying presence region of interests (RoIs) in frames such as rebar detection bridge deck inspection [4] , perform monitoring tasks for early warning of natural disasters [5] or crack detection [6] .…”

Section: Methods Detailsmentioning

confidence: 99%

Video Surveillance Processing Algorithms utilizing Artificial Intelligent (AI) for Unmanned Autonomous Vehicles (UAVs)

Nguyen

Truong

2021

MethodsX

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With the advancement of science and technology, the combination of the unmanned aerial vehicle (UAV) and camera surveillance systems (CSS) is currently a promising solution for practical applications related to security and surveillance operations. However, one of the biggest risks and challenges for the UAV-CSS is analysis, process, and transmission data, especially, the limitations of computational capacity, storage and overloading the transmission bandwidth. Regard to conventional methods, almost the data collected from UAVs is processed and transmitted that cost huge energy. A certain amount of data is redundant and not necessary to be processed or transmitted. This paper proposes an efficient algorithm to optimize the transmission and reception of data in UAV-CSS systems, based on the platforms of artificial intelligence (AI) for data processing. The algorithm creates an initial background frame and update to the complete background which is sent to server. It splits the region of interest (moving objects) in the scene and then sends only the changes. This supports the CSS to reduce significantly either data storage or data transmission. In addition, the complexity of the systems could be significantly reduced. The main contributions of the algorithm can be listed as follows; The developed solution can reduce data transmission significantly. The solution can empower smart manufacturing via camera surveillance. Simulation results have validated practical viability of this approach. The experimental method results show that reducing up to 80% of storage capacity and transmission data.

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Section: Methods Detailsmentioning

confidence: 99%

Video Surveillance Processing Algorithms utilizing Artificial Intelligent (AI) for Unmanned Autonomous Vehicles (UAVs)

Nguyen

Truong

2021

MethodsX

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“…Singh et al [15] suggest using drones for surveillance of violent individuals using ScatterNet Hybrid Deep Learning Network. Their system detects the violent individuals in real-time by processing the drone images in the cloud.…”

Section: Related Workmentioning

confidence: 99%

Improving Response Time for Crowd Management in Hajj

2021

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Flying Adhoc Network (FANET) is a particular type of Mobile Adhoc Network (MANET) that consists of flying drones or unmanned aerial vehicles (UAVs). MANETs are especially useful in rural and remote areas, where the lack of public networks necessitates data delivery through mobile nodes. Additionally, FANETs provide better coverage where there is a lack of roads. Generally, the goal of FANETs is to provide multimedia data to applications such as search and rescue operations, forest fire detection, surveillance and patrol, environmental monitoring, and traffic and urban monitoring. The above applications’ performance and efficiency depend on the quality and timely delivery of these essential data from an area of interest to control centers. This paper presents a Priority-based Routing Framework for Flying Adhoc Networks (PRoFFAN) for the expedited delivery of essential multimedia data to control centers. PRoFFAN reduces the FANET application’s response time by prioritizing the sending and forwarding of critical image data from the UAV to the control center. Our motivation application is crowd management; we believe that having important image features as early as possible will save lives and enhance the crowd’s safety and flow. We integrated PRoFFAN over the RPL routing layer of Contiki-NG’s IPv6 network stack. We used simulations in Cooja to demonstrate the benefit of PRoFFAN over conventional ZigBee.

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“…The proposed model detects people, extracts the pose of each person, and then matches it with the pose in a suspicious action dataset. [18] proposes the aerial surveillance of dangerous situations in a public area using object detection through drones. Visual surveillance is performed by defining five violent activities, i.e., punching, stabbing, shooting, kicking, and strangling, and detecting the defined activities through the drone.…”

Section: Object Detection Approach For Danger Detectionmentioning

confidence: 99%

Detection of Dangerous Situations using Deep Learning Model with Relational Inference

Jang¹,

Battulga²,

Nasridinov³

2020

J Multimed Inf Syst

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Crime has become one of the major problems in modern society. Even though visual surveillances through closed-circuit television (CCTV) is extensively used for solving crime, the number of crimes has not decreased. This is because there is insufficient workforce for performing 24-hour surveillance. In addition, CCTV surveillance by humans is not efficient for detecting dangerous situations owing to accuracy issues. In this paper, we propose the autonomous detection of dangerous situations in CCTV scenes using a deep learning model with relational inference. The main feature of the proposed method is that it can simultaneously perform object detection and relational inference to determine the danger of the situations captured by CCTV. This enables us to efficiently classify dangerous situations by inferring the relationshi p between detected objects (i.e., distance and position). Experimental results demonstrate that the proposed method outperforms existing methods in terms of the accuracy of image classification and the false alarm rate even when object detection accuracy is low.

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Eye in the Sky: Real-Time Drone Surveillance System (DSS) for Violent Individuals Identification Using ScatterNet Hybrid Deep Learning Network

Cited by 117 publications

References 25 publications

Video Surveillance Processing Algorithms utilizing Artificial Intelligent (AI) for Unmanned Autonomous Vehicles (UAVs)

Video Surveillance Processing Algorithms utilizing Artificial Intelligent (AI) for Unmanned Autonomous Vehicles (UAVs)

Improving Response Time for Crowd Management in Hajj

Detection of Dangerous Situations using Deep Learning Model with Relational Inference

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